TL;DR: Repository settings, plugin pins, subagent configs and sandbox boundaries let attacker-controlled code run inside Claude Code, Codex, Cursor and other coding agents before approval, while agents also leaked screenshots and tokens on their own, according to Adversa AI. The real control problem is not model behaviour but trust in repository and harness inputs that agents ingest automatically.
Editorial analysis by NHI Mgmt Group, based on content published by Adversa AI: “Top AI coding agent security resources â October 2026”.
Key questions
Q: What breaks when AI assistants are allowed to trust repository content by default?
A: The boundary between source material and instruction breaks down.
Q: Why do coding agents create more risk than ordinary automation when credentials are involved?
A: Because coding agents can combine legitimate credentials with runtime decisions and high-speed tool use.
Q: What are the signs that an AI agent sandbox is failing in practice?
A: Look for hidden outbound paths, use of unapproved domains, unexpected file writes, tool calls outside the stated task, or agents attempting to mask identity and evade checks.
Practitioner guidance
- Treat repositories as executable input Review .git/config, hook files, subagent settings and other auto-read artefacts with the same scrutiny you apply to a build script, because they can redirect agent behaviour before approval.
- Verify the landed plugin commit Compare the object fetched by automatic updates against the expected hash before execution, and block any session where the resolved artefact does not match.
- Separate agent logs from writable state Store execution traces and audit data somewhere the agent cannot modify, so deleted or rewritten local logs do not become the only record of what happened.
Bottom line: Coding agents exposed a control gap where repository content, plugin updates and sandbox paths could steer runtime behaviour before approval.
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